Visualising positional moves in chess

rm(list = ls())

set.seed(321) # For reproducibility

# Loading packages -------------------------------------------------------------
library(dplyr)            # Data manipulation
library(tidyr)            # Data pivoting
library(ggplot2)          # Visualisation
library(furrr)            # Parallel processing 
library(kableExtra)       # Creating tables
library(rchess)           # Working with chess objects
library(ggeasy)           # Makes theming plots easier

# Reading in data --------------------------------------------------------------
data <- tidytuesdayR::tt_load(2024, week = 40)
data <- data$chess

This document analyzes a dataset of chess games from Lichess provided by #TidyTuesday. It contains over 20,000 games, including information such as player ratings, move sequences, and other metrics. The main focus of this analysis will be on the specific moves played during each game.

Chess Data

The dataset includes the move sequences made by each player, represented as a string of moves. Below is a preview of the first two rows of the dataset.

data %>%
  select(game_id, moves) %>%
  mutate(game_id = seq(1:nrow(data))) %>%
  rename(ID = game_id, Moves = moves) %>%
  head(n = 2) %>%
  kbl(align = "c") %>%
  kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"))

ID	Moves
1	d4 d5 c4 c6 cxd5 e6 dxe6 fxe6 Nf3 Bb4+ Nc3 Ba5 Bf4
2	d4 Nc6 e4 e5 f4 f6 dxe5 fxe5 fxe5 Nxe5 Qd4 Nc6 Qe5+ Nxe5 c4 Bb4+

Each row displays the move sequence for a game, starting with White’s move. For example, the Slav Defense (1. d4 d5 2. c4 c6) or the Nimzowitsch Defense (1. d4 Nc6 2. e4 e5). Chess moves are typically recorded using Portable Game Notation (PGN), which makes it easy to replicate games.

PGN Conversion

To facilitate further analysis, we will convert the raw move strings into the PGN format, using a custom convert_to_pgn() function that I wrote.

# Convert moves into PGN format ------------------------------------------------
convert_to_pgn <- function(moves, game_id) {
  
  # Split the moves string into a list of individual moves
  move_list <- strsplit(moves, " ")[[1]]
  
  # Initialize an empty string for the PGN format
  pgn <- ""
  
  # Loop through the moves two at a time (each move is a pair: white and black)
  for (i in seq(1, length(move_list), by = 2)) {
    
    move_number <- (i + 1) / 2 # Move number calculation
    
    # Add both white's and black's moves, if available
    if (i < length(move_list)) {
      pgn <- paste0(pgn, move_number, ". ", move_list[i], " ", move_list[i+1], " ")
    } else {  # In case the game ends on white's move (no black move)
      pgn <- paste0(pgn, move_number, ". ", move_list[i])
    }
  }

  # Returning pgn string
  return(pgn)
}

# Converting moves into pgn format
chess_games <- data %>%
  select(game_id, moves) %>%
  mutate(
    game_id = seq(1:nrow(data)),
    moves = mapply(convert_to_pgn, moves, game_id))

# Displaying converted dataset
chess_games %>%
  rename(ID = game_id, Moves = moves) %>%
  head(n = 2) %>%
  kbl(align = "c") %>%
  kable_styling(
    full_width = FALSE,
    bootstrap_options = c("striped", "hover", "condensed", "responsive"))

ID	Moves
1	1. d4 d5 2. c4 c6 3. cxd5 e6 4. dxe6 fxe6 5. Nf3 Bb4+ 6. Nc3 Ba5 7. Bf4
2	1. d4 Nc6 2. e4 e5 3. f4 f6 4. dxe5 fxe5 5. fxe5 Nxe5 6. Qd4 Nc6 7. Qe5+ Nxe5 8. c4 Bb4+

Now that out data is in PGN format we can use the history_detail() from the rhcess package to extract the game history. However, as our dataset contains over 20,000 rows this will take some time. Hence, we will implement parallel processing using the furrr package.

# Function to extract game history
# process_moves <- function(p) {
#   chss <- Chess$new()
#   chss$load_pgn(p)
#   chss$history_detail()
# }
# 
# # Converting pgn format to game history
# chess_games <- chess_games %>%
#   mutate(data = future_map(moves, process_moves)) %>%
#   select(-moves) %>% 
#   tidyr::unnest(cols = c(data))
# 
# # Displaying converted dataset
# chess_games %>%
#   rename(ID = game_id) %>%
#   kbl(align = "c") %>%
#   kable_styling(
#     full_width = FALSE,
#     bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Creating a chess board

# Creating a chess board -------------------------------------------------------
board <- rchess:::.chessboarddata() %>%
  tibble() %>%
  select(cell, col, row, x, y, cc)

board %>%
  ggplot() +
  geom_tile(aes(x, y, fill = cc)) +
  scale_fill_manual(values =  c("burlywood3", "burlywood4")) + # Traditional board colours
  theme_void() +
  theme(axis.text = element_blank(), axis.ticks = element_blank()) +
  easy_remove_legend()

Figure 1: Chess Board